Instructions to use Yova/SmallCap7M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yova/SmallCap7M with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="Yova/SmallCap7M")# Load model directly from transformers import SmallCap model = SmallCap.from_pretrained("Yova/SmallCap7M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8c43d17a001703e9f0c7f79709f8823a379f5e2824efe0d971042f2420045891
- Size of remote file:
- 874 MB
- SHA256:
- 3f73bc26143943b17aaebe24c3b4a0cd87652717efd3e2aadc335c1cd3fcb92f
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